Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations95646
Missing cells75092
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 MiB
Average record size in memory65.0 B

Variable types

Numeric12
Categorical1
DateTime2
Boolean1

Alerts

assignment_id is highly overall correlated with idHigh correlation
avg_score is highly overall correlated with organised and 5 other fieldsHigh correlation
id is highly overall correlated with assignment_idHigh correlation
listing_id is highly overall correlated with owner_user_idHigh correlation
organised is highly overall correlated with avg_score and 4 other fieldsHigh correlation
overall_score is highly overall correlated with avg_score and 5 other fieldsHigh correlation
owner_user_id is highly overall correlated with listing_idHigh correlation
pet_care is highly overall correlated with avg_score and 1 other fieldsHigh correlation
profile_id is highly overall correlated with sitter_user_idHigh correlation
reliable is highly overall correlated with avg_score and 4 other fieldsHigh correlation
self_sufficient is highly overall correlated with avg_score and 4 other fieldsHigh correlation
sitter_user_id is highly overall correlated with profile_idHigh correlation
tidy is highly overall correlated with avg_score and 4 other fieldsHigh correlation
overall_score is highly imbalanced (93.2%) Imbalance
reply_ts has 74809 (78.2%) missing values Missing
assignment_id has unique values Unique
id has unique values Unique
pet_care has 2583 (2.7%) zeros Zeros

Reproduction

Analysis started2025-03-20 21:21:49.648327
Analysis finished2025-03-20 21:22:12.843702
Duration23.2 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

assignment_id
Real number (ℝ)

High correlation  Unique 

Distinct95646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean604397.53
Minimum498387
Maximum707534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:12.967297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum498387
5-th percentile508131.5
Q1551217.25
median606784
Q3657282.75
95-th percentile697575.75
Maximum707534
Range209147
Interquartile range (IQR)106065.5

Descriptive statistics

Standard deviation60881.941
Coefficient of variation (CV)0.10073162
Kurtosis-1.2095181
Mean604397.53
Median Absolute Deviation (MAD)52811.5
Skewness-0.057637321
Sum5.7808206 × 1010
Variance3.7066107 × 109
MonotonicityNot monotonic
2025-03-20T21:22:13.150472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
498711 1
 
< 0.1%
628171 1
 
< 0.1%
583566 1
 
< 0.1%
647879 1
 
< 0.1%
637583 1
 
< 0.1%
642505 1
 
< 0.1%
615066 1
 
< 0.1%
621289 1
 
< 0.1%
551236 1
 
< 0.1%
621303 1
 
< 0.1%
Other values (95636) 95636
> 99.9%
ValueCountFrequency (%)
498387 1
< 0.1%
498388 1
< 0.1%
498391 1
< 0.1%
498392 1
< 0.1%
498393 1
< 0.1%
498395 1
< 0.1%
498396 1
< 0.1%
498398 1
< 0.1%
498404 1
< 0.1%
498408 1
< 0.1%
ValueCountFrequency (%)
707534 1
< 0.1%
707531 1
< 0.1%
707530 1
< 0.1%
707529 1
< 0.1%
707524 1
< 0.1%
707523 1
< 0.1%
707519 1
< 0.1%
707518 1
< 0.1%
707517 1
< 0.1%
707516 1
< 0.1%

avg_score
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8944127
Minimum0
Maximum5
Zeros207
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2025-03-20T21:22:13.292831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45984305
Coefficient of variation (CV)0.093952652
Kurtosis52.144203
Mean4.8944127
Median Absolute Deviation (MAD)0
Skewness-6.5017339
Sum468131
Variance0.21145563
MonotonicityNot monotonic
2025-03-20T21:22:13.429634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 88485
92.5%
4 5861
 
6.1%
3 475
 
0.5%
1 399
 
0.4%
2 219
 
0.2%
0 207
 
0.2%
ValueCountFrequency (%)
0 207
 
0.2%
1 399
 
0.4%
2 219
 
0.2%
3 475
 
0.5%
4 5861
 
6.1%
5 88485
92.5%
ValueCountFrequency (%)
5 88485
92.5%
4 5861
 
6.1%
3 475
 
0.5%
2 219
 
0.2%
1 399
 
0.4%
0 207
 
0.2%

id
Real number (ℝ)

High correlation  Unique 

Distinct95646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237203.72
Minimum178299
Maximum527859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:13.590389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum178299
5-th percentile188657.5
Q1209777.25
median234603.5
Q3258981.75
95-th percentile295240.25
Maximum527859
Range349560
Interquartile range (IQR)49204.5

Descriptive statistics

Standard deviation35665.652
Coefficient of variation (CV)0.15035874
Kurtosis2.4660334
Mean237203.72
Median Absolute Deviation (MAD)24598.5
Skewness1.0627007
Sum2.2687587 × 1010
Variance1.2720388 × 109
MonotonicityNot monotonic
2025-03-20T21:22:13.779282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178794 1
 
< 0.1%
235798 1
 
< 0.1%
235826 1
 
< 0.1%
235820 1
 
< 0.1%
235814 1
 
< 0.1%
235809 1
 
< 0.1%
235808 1
 
< 0.1%
235805 1
 
< 0.1%
235803 1
 
< 0.1%
235800 1
 
< 0.1%
Other values (95636) 95636
> 99.9%
ValueCountFrequency (%)
178299 1
< 0.1%
178576 1
< 0.1%
178794 1
< 0.1%
178849 1
< 0.1%
178858 1
< 0.1%
178898 1
< 0.1%
178930 1
< 0.1%
179204 1
< 0.1%
179220 1
< 0.1%
179288 1
< 0.1%
ValueCountFrequency (%)
527859 1
< 0.1%
500329 1
< 0.1%
497644 1
< 0.1%
497297 1
< 0.1%
478418 1
< 0.1%
452735 1
< 0.1%
451749 1
< 0.1%
443666 1
< 0.1%
438161 1
< 0.1%
436327 1
< 0.1%

listing_id
Real number (ℝ)

High correlation 

Distinct45944
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623778.72
Minimum201
Maximum1180911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:13.967024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile108449.5
Q1420451.25
median640114.5
Q3835634
95-th percentile1086076
Maximum1180911
Range1180710
Interquartile range (IQR)415182.75

Descriptive statistics

Standard deviation288005.31
Coefficient of variation (CV)0.46171069
Kurtosis-0.69733491
Mean623778.72
Median Absolute Deviation (MAD)207708.5
Skewness-0.20268702
Sum5.9661939 × 1010
Variance8.2947056 × 1010
MonotonicityNot monotonic
2025-03-20T21:22:14.168547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511781 44
 
< 0.1%
358546 30
 
< 0.1%
359941 28
 
< 0.1%
266890 28
 
< 0.1%
388227 24
 
< 0.1%
554743 19
 
< 0.1%
571459 19
 
< 0.1%
698758 18
 
< 0.1%
675061 17
 
< 0.1%
188790 17
 
< 0.1%
Other values (45934) 95402
99.7%
ValueCountFrequency (%)
201 2
< 0.1%
439 1
 
< 0.1%
776 4
< 0.1%
939 1
 
< 0.1%
1033 3
< 0.1%
1041 3
< 0.1%
1052 1
 
< 0.1%
1310 1
 
< 0.1%
1338 2
< 0.1%
1387 2
< 0.1%
ValueCountFrequency (%)
1180911 1
< 0.1%
1180818 2
< 0.1%
1180813 1
< 0.1%
1180765 1
< 0.1%
1180669 1
< 0.1%
1180602 1
< 0.1%
1180465 1
< 0.1%
1180386 1
< 0.1%
1180282 1
< 0.1%
1180228 2
< 0.1%

organised
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9488008
Minimum0
Maximum5
Zeros213
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:14.327441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39238767
Coefficient of variation (CV)0.079289446
Kurtosis98.894758
Mean4.9488008
Median Absolute Deviation (MAD)0
Skewness-9.5229302
Sum473333
Variance0.15396808
MonotonicityNot monotonic
2025-03-20T21:22:14.466413image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 93302
97.5%
4 1284
 
1.3%
1 355
 
0.4%
3 348
 
0.4%
0 213
 
0.2%
2 144
 
0.2%
ValueCountFrequency (%)
0 213
 
0.2%
1 355
 
0.4%
2 144
 
0.2%
3 348
 
0.4%
4 1284
 
1.3%
5 93302
97.5%
ValueCountFrequency (%)
5 93302
97.5%
4 1284
 
1.3%
3 348
 
0.4%
2 144
 
0.2%
1 355
 
0.4%
0 213
 
0.2%

overall_score
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing283
Missing (%)0.3%
Memory size747.4 KiB
5.0
93691 
4.0
 
849
3.0
 
314
1.0
 
304
2.0
 
205

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters286089
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 93691
98.0%
4.0 849
 
0.9%
3.0 314
 
0.3%
1.0 304
 
0.3%
2.0 205
 
0.2%
(Missing) 283
 
0.3%

Length

2025-03-20T21:22:14.619106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-20T21:22:14.748729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 93691
98.2%
4.0 849
 
0.9%
3.0 314
 
0.3%
1.0 304
 
0.3%
2.0 205
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 95363
33.3%
0 95363
33.3%
5 93691
32.7%
4 849
 
0.3%
3 314
 
0.1%
1 304
 
0.1%
2 205
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 95363
33.3%
0 95363
33.3%
5 93691
32.7%
4 849
 
0.3%
3 314
 
0.1%
1 304
 
0.1%
2 205
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 95363
33.3%
0 95363
33.3%
5 93691
32.7%
4 849
 
0.3%
3 314
 
0.1%
1 304
 
0.1%
2 205
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 95363
33.3%
0 95363
33.3%
5 93691
32.7%
4 849
 
0.3%
3 314
 
0.1%
1 304
 
0.1%
2 205
 
0.1%

owner_user_id
Real number (ℝ)

High correlation 

Distinct45943
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2738604
Minimum308
Maximum5251116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:14.915828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum308
5-th percentile276045.75
Q11660317
median3028771
Q33706753.8
95-th percentile4750556.5
Maximum5251116
Range5250808
Interquartile range (IQR)2046436.8

Descriptive statistics

Standard deviation1363127.7
Coefficient of variation (CV)0.49774547
Kurtosis-0.83343424
Mean2738604
Median Absolute Deviation (MAD)913394.5
Skewness-0.39548139
Sum2.6193652 × 1011
Variance1.8581172 × 1012
MonotonicityNot monotonic
2025-03-20T21:22:15.111914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2547340 44
 
< 0.1%
1410478 30
 
< 0.1%
1419165 28
 
< 0.1%
947672 28
 
< 0.1%
1583695 24
 
< 0.1%
1024226 19
 
< 0.1%
2759380 19
 
< 0.1%
3316721 18
 
< 0.1%
2649559 17
 
< 0.1%
3221912 17
 
< 0.1%
Other values (45933) 95402
99.7%
ValueCountFrequency (%)
308 2
 
< 0.1%
685 2
 
< 0.1%
715 1
 
< 0.1%
768 11
< 0.1%
777 1
 
< 0.1%
1276 4
 
< 0.1%
1549 1
 
< 0.1%
1688 3
 
< 0.1%
1704 3
 
< 0.1%
1723 1
 
< 0.1%
ValueCountFrequency (%)
5251116 1
< 0.1%
5250567 2
< 0.1%
5250548 1
< 0.1%
5250323 1
< 0.1%
5249861 1
< 0.1%
5249588 1
< 0.1%
5248805 1
< 0.1%
5248433 1
< 0.1%
5247912 1
< 0.1%
5247049 1
< 0.1%

pet_care
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8305522
Minimum0
Maximum5
Zeros2583
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2025-03-20T21:22:15.265652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.86160157
Coefficient of variation (CV)0.17836502
Kurtosis25.464264
Mean4.8305522
Median Absolute Deviation (MAD)0
Skewness-5.1851715
Sum462023
Variance0.74235726
MonotonicityNot monotonic
2025-03-20T21:22:15.404028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 91430
95.6%
0 2583
 
2.7%
4 880
 
0.9%
1 376
 
0.4%
3 223
 
0.2%
2 154
 
0.2%
ValueCountFrequency (%)
0 2583
 
2.7%
1 376
 
0.4%
2 154
 
0.2%
3 223
 
0.2%
4 880
 
0.9%
5 91430
95.6%
ValueCountFrequency (%)
5 91430
95.6%
4 880
 
0.9%
3 223
 
0.2%
2 154
 
0.2%
1 376
 
0.4%
0 2583
 
2.7%

profile_id
Real number (ℝ)

High correlation 

Distinct32188
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1544276.4
Minimum64
Maximum4220986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size747.4 KiB
2025-03-20T21:22:15.818090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile85124
Q1911542
median1769929
Q32141454.5
95-th percentile2668574
Maximum4220986
Range4220922
Interquartile range (IQR)1229912.5

Descriptive statistics

Standard deviation796975.49
Coefficient of variation (CV)0.51608344
Kurtosis-0.90926425
Mean1544276.4
Median Absolute Deviation (MAD)532610.5
Skewness-0.42104558
Sum1.4770386 × 1011
Variance6.3516993 × 1011
MonotonicityNot monotonic
2025-03-20T21:22:16.018696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202498 40
 
< 0.1%
1904532 37
 
< 0.1%
669325 34
 
< 0.1%
1030631 32
 
< 0.1%
313111 31
 
< 0.1%
459192 31
 
< 0.1%
1977563 30
 
< 0.1%
578888 29
 
< 0.1%
650868 29
 
< 0.1%
1011132 29
 
< 0.1%
Other values (32178) 95324
99.7%
ValueCountFrequency (%)
64 1
 
< 0.1%
81 1
 
< 0.1%
86 5
 
< 0.1%
89 3
 
< 0.1%
122 7
< 0.1%
123 2
 
< 0.1%
145 5
 
< 0.1%
150 13
< 0.1%
160 1
 
< 0.1%
165 2
 
< 0.1%
ValueCountFrequency (%)
4220986 1
< 0.1%
4184282 1
< 0.1%
3834616 1
< 0.1%
3825369 1
< 0.1%
3775324 1
< 0.1%
3770939 1
< 0.1%
3706519 1
< 0.1%
3660462 1
< 0.1%
3616438 1
< 0.1%
3616365 1
< 0.1%

reliable
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9492085
Minimum0
Maximum5
Zeros218
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size747.4 KiB
2025-03-20T21:22:16.170942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4102464
Coefficient of variation (CV)0.082891314
Kurtosis93.582627
Mean4.9492085
Median Absolute Deviation (MAD)0
Skewness-9.4126976
Sum473372
Variance0.16830211
MonotonicityNot monotonic
2025-03-20T21:22:16.311830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 93654
97.9%
4 880
 
0.9%
1 469
 
0.5%
3 263
 
0.3%
0 218
 
0.2%
2 162
 
0.2%
ValueCountFrequency (%)
0 218
 
0.2%
1 469
 
0.5%
2 162
 
0.2%
3 263
 
0.3%
4 880
 
0.9%
5 93654
97.9%
ValueCountFrequency (%)
5 93654
97.9%
4 880
 
0.9%
3 263
 
0.3%
2 162
 
0.2%
1 469
 
0.5%
0 218
 
0.2%

reply_ts
Date

Missing 

Distinct20831
Distinct (%)> 99.9%
Missing74809
Missing (%)78.2%
Memory size747.4 KiB
Minimum2022-01-10 16:37:32
Maximum2025-01-26 18:34:31
Invalid dates0
Invalid dates (%)0.0%
2025-03-20T21:22:16.482124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:16.679128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

requested
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.5 KiB
False
79952 
True
15694 
ValueCountFrequency (%)
False 79952
83.6%
True 15694
 
16.4%
2025-03-20T21:22:16.818483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Distinct95474
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size747.4 KiB
Minimum2022-01-05 14:54:19
Maximum2024-07-01 21:42:14
Invalid dates0
Invalid dates (%)0.0%
2025-03-20T21:22:16.965257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:17.172410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

self_sufficient
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9546766
Minimum0
Maximum5
Zeros226
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2025-03-20T21:22:17.333471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38341545
Coefficient of variation (CV)0.077384555
Kurtosis110.34098
Mean4.9546766
Median Absolute Deviation (MAD)0
Skewness-10.151958
Sum473895
Variance0.1470074
MonotonicityNot monotonic
2025-03-20T21:22:17.468670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 93758
98.0%
4 937
 
1.0%
1 340
 
0.4%
3 247
 
0.3%
0 226
 
0.2%
2 138
 
0.1%
ValueCountFrequency (%)
0 226
 
0.2%
1 340
 
0.4%
2 138
 
0.1%
3 247
 
0.3%
4 937
 
1.0%
5 93758
98.0%
ValueCountFrequency (%)
5 93758
98.0%
4 937
 
1.0%
3 247
 
0.3%
2 138
 
0.1%
1 340
 
0.4%
0 226
 
0.2%

sitter_user_id
Real number (ℝ)

High correlation 

Distinct32188
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2480899
Minimum116
Maximum7669735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size373.7 KiB
2025-03-20T21:22:17.636341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile100605
Q11175734
median2759074.5
Q33588862.5
95-th percentile4658456.5
Maximum7669735
Range7669619
Interquartile range (IQR)2413128.5

Descriptive statistics

Standard deviation1440045.2
Coefficient of variation (CV)0.58045298
Kurtosis-1.072023
Mean2480899
Median Absolute Deviation (MAD)1074942.5
Skewness-0.19386968
Sum2.3728807 × 1011
Variance2.0737302 × 1012
MonotonicityNot monotonic
2025-03-20T21:22:17.848446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
241891 40
 
< 0.1%
3081042 37
 
< 0.1%
782804 34
 
< 0.1%
1480173 32
 
< 0.1%
381412 31
 
< 0.1%
556301 31
 
< 0.1%
3237104 30
 
< 0.1%
704361 29
 
< 0.1%
793731 29
 
< 0.1%
1439899 29
 
< 0.1%
Other values (32178) 95324
99.7%
ValueCountFrequency (%)
116 1
 
< 0.1%
142 1
 
< 0.1%
148 5
 
< 0.1%
153 3
 
< 0.1%
195 7
< 0.1%
196 2
 
< 0.1%
225 5
 
< 0.1%
231 13
< 0.1%
239 1
 
< 0.1%
246 2
 
< 0.1%
ValueCountFrequency (%)
7669735 1
< 0.1%
7597001 1
< 0.1%
6905512 1
< 0.1%
6803051 1
< 0.1%
6794253 1
< 0.1%
6660837 1
< 0.1%
6568260 1
< 0.1%
6484454 1
< 0.1%
6484307 1
< 0.1%
6435647 1
< 0.1%

tidy
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9349999
Minimum0
Maximum5
Zeros228
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size93.5 KiB
2025-03-20T21:22:18.015034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42560249
Coefficient of variation (CV)0.086241641
Kurtosis78.554516
Mean4.9349999
Median Absolute Deviation (MAD)0
Skewness-8.4203592
Sum472013
Variance0.18113748
MonotonicityNot monotonic
2025-03-20T21:22:18.150782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 92387
96.6%
4 2002
 
2.1%
3 425
 
0.4%
1 413
 
0.4%
0 228
 
0.2%
2 191
 
0.2%
ValueCountFrequency (%)
0 228
 
0.2%
1 413
 
0.4%
2 191
 
0.2%
3 425
 
0.4%
4 2002
 
2.1%
5 92387
96.6%
ValueCountFrequency (%)
5 92387
96.6%
4 2002
 
2.1%
3 425
 
0.4%
2 191
 
0.2%
1 413
 
0.4%
0 228
 
0.2%

Interactions

2025-03-20T21:22:10.356852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:51.900011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.451720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.084645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.692305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.752046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.356175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.979355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.658639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.246423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.815665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.638443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.483840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.015320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.575715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.214750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.820195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.887994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.490158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.113167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.785334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.371770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.949629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.770687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.610942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.135895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.703933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.351792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.950816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.023244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.627654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.237303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.911460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.508455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.088035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.922010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.740809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.268853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.931921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.493258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:57.333122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.171316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.764745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.370054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.047901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.651681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.231408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.074774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.885891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.405455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.074878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.632527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:57.540497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.316415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.904476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.505508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.181079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.787789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.376073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.226031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.026979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.535250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.199247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.761538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:57.683365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.438077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.044747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.778856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.306397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.920216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.515114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.371447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.163593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.666718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.322980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:55.893459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:57.826081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.570556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.184091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:02.905052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.434042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.049347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.645910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.522200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.298609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.794488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.450639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.026631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.065541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.695171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.309468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.029352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.565508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.177918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.778922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.660325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.433580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:52.920998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.569520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.154038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.194625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.826465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.434053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.150404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.699075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.302038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:07.913041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.795931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.572002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.048366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.692314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.283959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.325879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:59.957265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.563476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.271013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.837851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.430450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.046648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:09.938229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.700625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.176168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.815031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.412774image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.457797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.090909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.689964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.393938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:04.971029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.553772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.185976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.075757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:11.843419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:53.322937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:54.955013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:56.556345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:21:58.612983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:00.230991image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:01.841930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:03.533083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:05.114174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:06.690179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:08.336365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-03-20T21:22:10.221605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2025-03-20T21:22:18.271291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
assignment_idavg_scoreidlisting_idorganisedoverall_scoreowner_user_idpet_careprofile_idreliablerequestedself_sufficientsitter_user_idtidy
assignment_id1.000-0.0010.8440.315-0.0100.0090.2940.0040.288-0.0110.035-0.0090.283-0.007
avg_score-0.0011.000-0.0250.0180.5720.6480.0180.761-0.0210.5300.0000.515-0.0220.670
id0.844-0.0251.0000.242-0.0320.0260.224-0.0160.210-0.0290.029-0.0280.205-0.030
listing_id0.3150.0180.2421.000-0.0110.0020.9520.0220.158-0.0120.123-0.0140.157-0.001
organised-0.0100.572-0.032-0.0111.0000.637-0.0100.406-0.0150.7010.0000.644-0.0150.590
overall_score0.0090.6480.0260.0020.6371.0000.0040.5590.0100.6130.0000.5880.0110.530
owner_user_id0.2940.0180.2240.952-0.0100.0041.0000.0220.152-0.0120.099-0.0120.1510.000
pet_care0.0040.761-0.0160.0220.4060.5590.0221.000-0.0090.4610.0000.410-0.0100.340
profile_id0.288-0.0210.2100.158-0.0150.0100.152-0.0091.000-0.0230.011-0.0140.987-0.031
reliable-0.0110.530-0.029-0.0120.7010.613-0.0120.461-0.0231.0000.0060.664-0.0230.541
requested0.0350.0000.0290.1230.0000.0000.0990.0000.0110.0061.0000.0000.0130.004
self_sufficient-0.0090.515-0.028-0.0140.6440.588-0.0120.410-0.0140.6640.0001.000-0.0140.531
sitter_user_id0.283-0.0220.2050.157-0.0150.0110.151-0.0100.987-0.0230.013-0.0141.000-0.032
tidy-0.0070.670-0.030-0.0010.5900.5300.0000.340-0.0310.5410.0040.531-0.0321.000

Missing values

2025-03-20T21:22:12.034237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-20T21:22:12.406149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-20T21:22:12.730773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

assignment_idavg_scoreidlisting_idorganisedoverall_scoreowner_user_idpet_careprofile_idreliablereply_tsrequestedreview_tsself_sufficientsitter_user_idtidy
0498711517879452929155.02632565518764285NaTTrue2022-01-08 01:50:27530165065
1498506517885848223055.092931755273935NaTFalse2022-01-08 14:20:5356366245
2498792517893065827755.031550105180021252023-01-20 20:40:01False2022-01-08 22:48:07528511395
3500794517957860587755.02972601514431805NaTFalse2022-01-12 19:38:37522216985
4499067517998552577155.0261150755319155NaTFalse2022-01-16 20:34:3556420365
5499888517998763217055.03066519518868585NaTFalse2022-01-16 20:38:39530404775
6498877518000455569055.01631733595636952022-01-18 17:35:07False2022-01-17 00:21:19513326685
7499916518001446156455.020817585167489252022-01-17 03:22:34False2022-01-17 03:06:28526158615
8498418518004151063355.0253840051613245NaTFalse2022-01-17 09:34:3351939915
9499538518017953985755.0176948459977725NaTFalse2022-01-18 06:21:30514113195
assignment_idavg_scoreidlisting_idorganisedoverall_scoreowner_user_idpet_careprofile_idreliablereply_tsrequestedreview_tsself_sufficientsitter_user_idtidy
95636555572540806817062855.059876655438425NaTFalse2023-12-06 19:56:3756578975
95637703022541125592431155.041022865196406152023-12-30 02:20:32False2023-12-14 12:33:56532075125
956387070845414434118009355.05246841529626395NaTFalse2023-12-23 09:04:33552547495
95639704845541744529211155.0103485651505485NaTTrue2023-12-30 12:09:1151815625
95640699810542083138774455.047997653608135NaTFalse2024-01-03 10:47:2754403075
95641705871542627913529155.0413242524600165NaTFalse2024-01-09 08:51:47533353445
95642703828543086132967955.0124579254641645NaTFalse2024-01-17 02:19:2955621105
95643645320543816117694255.06280305173805NaTTrue2024-02-03 00:35:255259055
95644707490547841825072355.06468875351513752024-04-12 21:53:27False2024-04-12 21:48:00562895235
956457057085497297117603355.043781895343975NaTFalse2024-05-11 00:35:465475625